PnPNet: End-to-End Perception and Prediction With Tracking in the Loop
A review of a CVPR paper in the paradigm of autonomous driving
In this post, we will be looking at the paper PnPNet: End-to-End Perception and Prediction with Tracking in the Loop, by Liang et al., which was published in CVPR 2020 [1]. After defining our task and discussing some related research in this field, we will be looking at the methodology of the paper. Then we will analyze the quantitative results and have a look at the qualitative results. Finally, we will finish it off with some remarks and possible ideas for extension.
Introduction
In the context of self-driving vehicles, predicting the motion of other vehicles is a critical task. Approximating the trajectory of neighboring agents in the future is equally as important as detecting them in the current time frame. To do this task, so far three paradigms have been proposed.
The first one divides this problem into three separate sub-tasks, which are handled by completely independent sub-systems. These three tasks, namely, object detection, object tracking, and motion forecasting are done sequentially. As they are developed separately, they need more computing power and cannot correct mistakes from upstream tasks. The second paradigm tries to solve the detection and prediction task with a single neural network. This yields more efficient computation but these models suffer from limited use of temporal history and are vulnerable to occlusion.
Three paradigms of perception and prediction problems [1]
This paper introduces the new third paradigm. It argues that, for sequential modeling tasks such as motion forecasting, past data is very important. To that end, it proposes PnPNet which combines multi-object tracking with joint perception and prediction models. We will go into the details of the model after discussing some other works that try to tackle different aspects of our problem.
Related works
3D object detection
The use of depth sensors such as LiDARs has been shown to have better performance [2] than cameras for 3D detection. Some works also explore a fusion of LiDAR point clouds and camera inputs [3].
A qualitative result of fusion models [3]
Multi-Object Tracking
Multi-Object tracking is a system to track multiple objects at the same time. It consists of a discrete data association problem and a continuous trajectory estimation problem [4]. There have been efforts to handle occlusion with hand-crafted heuristics [5] and single object tracking [6] to handle occlusion. To handle the trajectory problem, some approaches also use sensor features[7].
An example of multi-object tracking takes from [8]
Motion Forecasting
Different methods try to approach the multi-agent motion forecasting problem. Alahi et al. propose LSTM based social pooling to model motion [9]. Social-GAN [10] improves on it using adversarial training. The use of sensor features is also explored, but these methods usually have generalization issues when applied to noisy data [11]. Raster representation in BEV that encodes both perception and map information is widely used as well [2].
Joint models for Perception and Prediction
The FAF paper [12] of Luo et al. serves as a direct predecessor and an evaluation baseline for PnPNet. This model uses a single convolutional backbone to detect and predict future motion. NeuralMP [13] shares motion planning features with perception and prediction to allow end-to-end training.
Some qualitative results from NeuralMP [13]
Motivations for the approach
Research efforts in this sector have developed the field of autonomous driving a lot. However, they also have some shortcomings. Multi-object tracking with supplementary sensor readings can not effectively use a long history of temporal data. These models saturate after three seconds [7]. Also most-multi object tracking efforts only tackle one of the two tasks at a time.
Although earlier joint models for perception and prediction use sensor information, they do not exploit the salient features of actors in the time dimension. As a result, their overall performance might be vastly improved with some adjustments.
As we will see, PnPNet does exactly that. Where earlier models kept the tracking task at the end of the task stack, PnPNet pulls it inside the perception-prediction loop. By doing that, it can utilize trajectory-level representation and can improve performance in all related tasks. It also solves the discrete-continuous tracking problem that other works overlook, with a novel tracking framework. PnPNet deviates from the common approach of [2] and directly uses perception features for better scene contexts.
Proposed method: PnPNet
Technical Contributions
The discussed related research overlook an important aspect of our problem. They do not take the temporal characteristics of actors into account. To allow for that, this paper makes two major technical contributions :
- It introduces a new trajectory representation based on a sequence of detections through time.
- It proposes a multi-object tracker that solves both the association and the trajectory estimation problem.
Model Overview
PnPNet consists of three separate modules :
- 3D Object detection module
- Discrete-Continuous Tracking module
- Motion forecasting module
A summary of PnPNet workflow [1]
3D Object detection module
The detection module takes multi-sweep LiDAR point cloud representation in birds-eye-view and an HD map as input. Optionally, geometric and semantic information of the HD map can also be used. 2D convolutional neural network-based backbone with multi-scale feature fusion is applied to the input, which generates the intermediate BEV features. A convolutional detection header is then used on the intermediate features to create dense object detections at each time step. We maintain a database of intermediate features at each time step because they will be needed to in the tracking module downstream.
Workflow of the 3D detection module
Discrete-Continuous Tracking module
As discussed in the related works, there are two separate challenges in multi-object tracking. Previous works mostly focus on the discrete problem of data association but PnPNet also takes the continuous problem of trajectory estimation into account. The paper argues that it helps to prevent association errors from accumulating over time and reduces the variance in motion history. To that end, it proposes a two-stage tracking framework. To use the framework, rich and concise trajectory level object representation needs to be learned.
Trajectory level representation generation
The representation learning problem is formulated as a sequence modeling problem. We use the intermediate FEV features from the backbone network and the location information of each object at time t to run a Bilinear Interpolation. Note that the location information is obtained from a database of older trajectories. This output is regarded as the representation of the motion of each object from the start frame to the current frame. Along with this, the absolute velocities of each object are passed into an MLP/feed-forward network.
The merged features from this FF network are then used as the sequential input for our LSTM. This LSTM hidden state is our trajectory level representation at each time step.
Data Association
The first stage of the tracking framework is the discrete tracker. We obtain the dense detections from the 3D detection module upstream and the trajectories from the database of older tracks. At time t, given Nt detections and Mt-1 trajectories, it tries to determine the associations between them. This association problem is formulated as a bipartite graph matching problem. As a result, one-to-one matching is guaranteed. The edge values for our bipartite matching problem are the affinity values, which represent how likely is a track Mt-1 to belong to detection Nt. The affinity matrix :
$$
C_{i,j} =
\begin{cases}
MLP_{pair}(f(D_{i}^t); h(P_{j}^{t−1})) & if\ 1 ≤ j ≤ M_{t−1}\\
MLP_{unary}(f(D_{i}^t)) & if\ j = M_{t−1} + i \\
−inf & otherwise \\
\end{cases}
$$
The affinity values are calculated with binary or unary MLPs. If we have more or equal number of tracks at the previous step than detections at this step, we use the binary MLP. Otherwise, the unary MLP is used. This bipartite system is solved optimally using the Hungarian algorithm. We use single object tracking for older unmatched tracks. Combining results from bipartite matching and SOT yields a final set of unrefined tracks Pt. This set of unrefined tasks have the candidates for updating the database of older tracks to be used in the future time steps.
Associating tracks with detections
Single object tracking
The single object tracker used in the PnPNet paper follows nearly the same methodology as the Siamese tracker [14]. The Siamese tracker has two networks. One of these twin networks receives an exemplar image as input, another one receives a search image as input. The task for the twin network is to try and find the exemplar image within the search image.
Siamese tracker from [14].
Siamese trackers usually have a cross-correlation layer at the end. PnPNet chooses to replace this layer with an MLP with learnable parameters.
Hungarian Algorithm
The Hungarian algorithm is an optimization algorithm that produces the best one-to-one matching when applied to a bipartite graph. In our context, two sets of nodes are the detections and the tracks, denoted as Ni and Mj respectively. The edges between these sets of nodes are the affinity values, denoted by ak. Here we see an example formulation :
graph TD;
id1(("N1"))-- "a1" ---id2(("M1"));
id1(("N1"))-- "a4" ---id3(("M2"));
id1(("N1"))-- "a7" ---id4(("M3"));
id6(("N2"))-- "a2" ---id2(("M1"));
id6(("N2"))-- "a5" ---id3(("M2"));
id6(("N2"))-- "a8" ---id4(("M3"));
id7(("N3"))-- "a3" ---id2(("M1"));
id7(("N3"))-- "a6" ---id3(("M2"));
id7(("N3"))-- "a9" ---id4(("M3"));
Hungarian algorithm will find the best matching that maximizes the overall affinity value throughout the graph.
Trajectory Estimation
Trajectory estimation is the second stage in our two-stage tracking network. This module re-evaluates each object track for the new observation at the current frame and helps minimize false positives and localization errors from upstream tasks. The LSTM representation is refined by an MLP according to the current association. The MLP outputs a confidence score and center position offset. $$ score_{i}, \Delta u_{i}^{t-T_0+1:t}, \Delta v_{i}^{t-T_0+1:t} = MLP_{refine}(h(P_{i}^t)) $$ Using these confidence scores, a non-maximal suppression is applied and only top Mt out of Pt tracks are accepted. Other tracks are discarded as false positives. The database of trajectories is then updated with this accepted tracks set.
Trajectory estimation sub-module.
Motion Forecasting module
Earlier joint perception and prediction models design the prediction module as another convolutional header. However, PnPnet puts tracking on the detection backbone and creates object trajectory representation as LSTM hidden states. These hidden states are then passed through an MLP to estimate motion prediction for each object we are tracking in each time step. $$ \Delta u_{i}^{t:t+\Delta T}, \Delta v_{i}^{t:t+\Delta T} = MLP_{refine}(h(P_{i}^t)) $$ Here $\Delta T$ is a parameter of the model that denotes the prediction horizon.
End-to-End Training
As the model has a lot of moving parts, it is hard to visualize how the individual modules interact with each other. So here is an illustration that summarizes the workflow:
Each rectangular box represents a major process. These boxes are color coded according to their individual modules. A database of tracks is represented with the cylindrical icon.
As we can see from the figure, PnPNet only has one set of inputs and one set of outputs. This means it can be trained end-to-end.
To train PnPNet end-to-end, a multi-task loss is calculated from the individual losses of its three modules. $$ L = L_{detect}+ L_{track} + L_{predict} $$ Here the detection loss is the cross-entropy loss and the smoothed l1 loss over bounding boxes. The tracking loss is again another multi-task loss defined as : $$ L_{track} = L_{score}^{affinity} + L_{score}^{sot} + L_{score}^{refine} + L_{reg}^{refine} $$ The max-margin loss is applied to the sot scores, the trajectory scores, and the affinity matrix. $$ L_{score} = \frac{1}{N_{i,j}} \sum_{i \epsilon{pos}, j \epsilon{neg}} max(0, m − (a_i − a_j)) $$ Here Ni,j is the total number of positive and negative pairs, where ai and aj are scores for positive and negative samples accordingly.
For the prediction task, the loss is just the smoothed l1 loss of the predictions.
The Adam optimizer is used to train PnPNet with a frame rate of 10Hz. At each frame maximum M = 50 tracks and N = 50 detections are maintained. The prediction horizon is set $\Delta T$= 3 seconds with 0.5 seconds interval.
Datasets and Metrics
The PnPNet paper tests itself on two major driving datasets. The performance of PnPNet is measured from modular metrics of detection and tracking, as well as from system metrics for end-to-end perception and prediction.
nuScenes Dataset [15]
This dataset consists of 1000 20-seconds long log snippets with LiDAR sweeps at 20Hz and 3D labels for objects and corresponding HD maps. Although this dataset has some caveats because it only has 84 unique driving journeys, and 63.5% of the cars are parked.
An example from nuScences [15]
ATG4D Dataset [16]
To simulate real-world driving scenario, the authors validate PnPNet on the ATG4D dataset. It contains over 5000 log snippets of 1000 unique journeys. Each snippet consists of 64 beam LiDAR sweeps at 10Hz and the corresponding HD maps. Also, only 48.1% of the cars are parked.
Modular Metrics
For detection and tracking tasks, PnPNet follows tracking metrics introduced by nuScenes [15]. Detection task is measured in terms of average precision (AP) and tracking task in terms of MOT metrics, defined by Bernardin et al.[17].
System Metrics
Unlike predefined modular metrics, system metrics are defined by the PnPNet paper itself. For the perception task, average precision and maximum recall are used. For the prediction task, PnPNet uses average displacement error (ADE) and final displacement error (FDE). Both of these displacement errors are measured over 3 seconds, with ADE over a 0.5-second interval. Displacement error is simply the difference between the actual and the predicted location of an object.
Results
In this section, we look at the quantitative results, some ablation studies, and some qualitative results on PnPnet.
Quantitative results
Detection and tracking
The detection module is evaluated on nuScenes in comparison to other SOA detections. PnPnet outperforms leading model Megvii[18] in most metrics. The tracking module is evaluated against the leading approach StanfordIPRL-TRI[19] and a PnPNet baseline with KF tracker replacing the tracking module. PnPnet outperforms [19] and KF tracker in MOT metrics.
Evaluation of multi object tracking on nuScenes [1]
Even though there are not many significant changes in the object detection module, PnPNet being an end-to-end model benefits this task too. A Better prediction model also fine-tunes the detection module. Improvements in the tracking model then influence the prediction task.
Perception and prediction
PnPnet is evaluated on both nuScenes and ATG4D for end-to-end perception and prediction. The paper establishes a baseline by removing the tracking module and denotes it as “PnPnet w/o track”. This implementation can be considered a re-implementation of [12]. The fast and furious (FAF)[12] is the direct predecessor of PnPNet. Classic PnPNet outperforms this baseline consistently across both datasets and all metrics, illustrating that keeping tracking in the loop allows for massive improvements in all tasks.
End-to-End results on ATG4D and nuScenes [1]
PnPNet observes up to 4.4 and 2.3 percentage increase in average precision and maximum recall respectively. For the prediction task, ADE is decreased up to 20% and FDE up to 15%.
Ablation studies
Ablation studies are often conducted on end-to-end models to find out the individual contributions to the final result. PnPNet paper also conducts some ablation studies by removing motion features, sot, trajectory refinement, and the whole track module one by one.
Ablation studies on PnPNet [1]
From the results of the studies, it is obvious that the tracking module has the largest effect on the prediction results. ADE and FDE go up to 18% percent in the absence of the tracking module. The trajectory rescore module influences average precision and displacement errors. The single object tracking module has a decent contribution across all the metrics.
Qualitative Results
Well, these explanations are good and all but, where are the tangible results? Let's look at the visualization of perception and prediction results on ATG4D. This demonstrates the fact that by learning trajectory representations explicitly, PnPNet is able to handle occlusion and produces robust predictions.
Qualitative results of [1]
This video was taken from the CVF youtube channel. The original video was the presentation for the PnPNet paper, which can be found here.
Summary
Earlier works in the realm of perception and prediction of autonomous driving have somewhat overlooked the contribution of trajectory information for future predictions. Although recent works do capitalize on the use of joint models, they leave tracking out of the loop. Following the footstep of these recent developments, PnPNet proposes a novel end-to-end training framework for perception and prediction while taking tracking into consideration. They also introduce a new tracking framework that can be looped into classic detection-prediction stacks. Their proposed improvements are then validated on different datasets and compared with other leading models in this paradigm. Finally, they show significant improvement in all three tasks as well as end-to-end performance.
Personal Remarks
- In the paper, the authors assume the maximum number of detection and tracks to be 50. But, on busy roads usually there are far more moving objects within close proximity. The only way for PnPNet to consider this is to increase the N and M parameters. However, this again results in a massive number of computations for the Hungarian algorithm. The paper doesn't take this problem into account.
- The authors only store tracks up to 16 older frames to maintain real-time performance. However, ablation studies show that, keeping tracks up to 32 frames achieves better prediction result. This points to the fact that this model has some performance-efficiency trade-off.
- The predecessor of PnPNet, FaF[12] did not have an explicit tracking module, but still outperformed the Hungarian matching. PnPNet, however, incorporates the Hungarian algorithm in it's design and outperforms FaF. We can posit that continuous track refinement has helped in the association task as well.
- PnPNet uses an LSTM unit in the tracking module. However, as we know LSTMs need quite a bit of computation resources. In future works, other less expensive recurrent units can be explored for the same task. For example, GRU units [20] are computationally less expensive and have been proven to be comparable in terms of performance [21].
- Recently many sequence modeling tasks are leveraging the attention mechanism. As we define our tracking as a sequential problem, it may be useful to apply self-attention on top of the recurrent unit outputs. Although this is not a pure sequence problem, self-attention may refine and provide importance to better trajectory features. Also, built-in attention [22] can be applied to the 2D-Convolutional backbone of the 3D detection algorithm.
References
[1] Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, Raquel Urtasun, "PnPNet: End-to-End Perception and Prediction with Tracking in the Loop", in CVPR, 2020.
[2] Bin Yang, Wenjie Luo, and Raquel Urtasun. Pixor: Realtime 3d object detection from point clouds. In CVPR, 2018.
[3] Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun. Deep continuous fusion for multi-sensor 3d object detection. In ECCV, 2018.
[4] Anton Milan, Konrad Schindler, and Stefan Roth. Multitarget tracking by discrete-continuous energy minimization. TPAMI, 38(10):2054–2068, 2015
[5] Hasith Karunasekera, Han Wang, and Handuo Zhang. Multiple object tracking with attention to appearance, structure, motion and size. IEEE Access 7:104423–104434, 2019.
[6] Peng Chu and Haibin Ling. Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In ICCV, 2019.
[7] Wenwei Zhang, Hui Zhou, Shuyang Sun, Zhe Wang, Jianping Shi, and Chen Change Loy. Robust multi-modality multi-object tracking. In ICCV, 2019.
[8] Michael D Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, and Luc Van Gool. Online multiperson tracking-by-detection from a single, uncalibrated camera. TPAMI, 33(9):1820–1833, 2010.
[9] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. Social lstm: Human trajectory prediction in crowded spaces. In CVPR, 2016.
[10] Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi. Social gan: Socially acceptable trajectories with generative adversarial networks. In CVPR, 2018.
[11] Nicholas Rhinehart, Rowan McAllister, Kris Kitani, and Sergey Levine. Precog: Prediction conditioned on goals invisual multi-agent settings. In ICCV, 2019.
[12] Wenjie Luo, Bin Yang, and Raquel Urtasun. Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In CVPR, 2018.
[13] Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun. End-to-end interpretable neural motion planner. In CVPR, 2019.
[14] Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. Fully-convolutional siamese networks for object tracking. In ECCV, 2016.
[15] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020.
[16] Bin Yang, Wenjie Luo, and Raquel Urtasun. Pixor: Realtime 3d object detection from point clouds. In CVPR, 2018.
[17] Keni Bernardin, Alexander Elbs, and Rainer Stiefelhagen. Multiple object tracking performance metrics and evaluation in a smart room environment. In Sixth IEEE International Workshop on Visual Surveillance, in conjunction with ECCV, 2006.
[18] Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, and Gang Yu. Class-balanced grouping and sampling for point cloud 3d object detection. arXiv preprint arXiv:1908.09492, 2019
[19] Hsu-kuang Chiu, Antonio Prioletti, Jie Li, and Jeannette Bohg. Probabilistic 3d multi-object tracking for autonomous driving. arXiv preprint arXiv:2001.05673, 2020.
[20] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, In EMNLP 2014.
[21] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. In NIPS 2014 Deep Learning and Representation Learning Workshop.
[22] Niamul Quader, Md Mafijul Islam Bhuiyan, Juwei Lu, Peng Dai,and Wei Li. Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks. In ECCV 2020.


@Controller
@RequestMapping(value="admin/")
public class AdminController
extends java.lang.Object
Handles mappings of $PASTAUrl$/admin/...
Both students and teaching staff can access this url. Students have highly limited functionality. If the authentication system is not using the database, then it's an empty page.
| Modifier and Type | Field and Description |
|---|---|
protected org.apache.commons.logging.Log |
logger |
private UserManager |
userManager |
| Constructor and Description |
|---|
AdminController() |
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
changeAuthType(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String type,
java.lang.String[] address)
$PASTAUrl$/admin/auth/
|
java.lang.String |
changePassword(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
ChangePasswordForm form)
$PASTAUrl$/admin/changePassword/
|
java.lang.String |
deleteUser(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String username)
$PASTAUrl$/admin/delete/{username}/
|
java.lang.String |
get(org.springframework.ui.ModelMap model)
$PASTAUrl$/admin/
|
PASTAUser |
getUser()
Get the current logged in user.
|
java.lang.String |
replaceStudents(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String list)
$PASTAUrl$/admin/replaceStudents/ - POST
|
java.lang.String |
replaceTutors(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String list)
$PASTAUrl$/admin/replaceTutors/ - POST
|
ChangePasswordForm |
returnNewUnitTestModel()
Return the empty form for changing your password (DB authentication only)
|
void |
setMyService(UserManager myService) |
java.lang.String |
updateStudents(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String list)
$PASTAUrl$/admin/updateStudents/ - POST
|
java.lang.String |
updateTutors(org.springframework.ui.ModelMap model,
javax.servlet.http.HttpServletRequest request,
java.lang.String list)
$PASTAUrl$/admin/updateTutors/ - POST
|
protected final org.apache.commons.logging.Log logger
private UserManager userManager
public void setMyService(UserManager myService)
@ModelAttribute(value="changePasswordForm") public ChangePasswordForm returnNewUnitTestModel()
public PASTAUser getUser()
Gets the "user" attribute from the current session and gets the user with
that username from the UserManager
.
UserManager.getUser(String)
@RequestMapping(value="", method=GET) public java.lang.String get(org.springframework.ui.ModelMap model)
Ensure user exists and has authenticated against the system. If they have not, redirect them to the login screen.
Attributes:
| authType | ProjectProperties.getInstance().getAuthenticationValidator().getClass().getName() |
| unikey | PASTAUser currently logged in user |
| people | Only if tutor or instructor! - the list of users (both students and teaching staff) |
| addresses | Only if tutor or instructor! - the list of sever addresses for which to authenticate against |
model
- the model@RequestMapping(value="/changePassword/", method=POST) public java.lang.String changePassword(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @ModelAttribute(value="changePasswordForm") ChangePasswordForm form)
Change the password of a user.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themform
- the change password form@RequestMapping(value="/replaceStudents/", method=POST) public java.lang.String replaceStudents(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @RequestParam(value="list") java.lang.String list)
Replace the list of students with a new one.
Parse the csv format that is passed in (e.g. username,stream,class), turn into
PASTAUser
and then call UserManager.replaceStudents(List)
.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themlist
- the list of students which will be used to replace (csv format).@RequestMapping(value="/updateStudents/", method=POST) public java.lang.String updateStudents(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @RequestParam(value="list") java.lang.String list)
Update the list of students with a new one.
Parse the csv format that is passed in (e.g. username,stream,class), turn into
PASTAUser
and then call UserManager.updateStudents(List)
.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themlist
- the list of students which will be used to replace (csv format).@RequestMapping(value="/replaceTutors/", method=POST) public java.lang.String replaceTutors(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @RequestParam(value="list") java.lang.String list)
Replace the list of teaching staff with a new one.
Parse the csv format that is passed in (e.g. username,permissionlevel,class1,class2,...,classN), turn into
PASTAUser
and then call UserManager.replaceTutors(List)
.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themlist
- the list of teaching staff which will be used to replace (csv format).@RequestMapping(value="/updateTutors/", method=POST) public java.lang.String updateTutors(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @RequestParam(value="list") java.lang.String list)
Update the list of teaching staff with a new one.
Parse the csv format that is passed in (e.g. username,permissionlevel,class1,class2,...,classN), turn into
PASTAUser
and then call UserManager.updateTutors(List)
.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themlist
- the list of teaching staff which will be used to replace (csv format).@RequestMapping(value="/delete/{username}/") public java.lang.String deleteUser(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @PathVariable(value="username") java.lang.String username)
Delete a user from the system.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themusername
- the name of the user@RequestMapping(value="/auth/", method=GET) public java.lang.String changeAuthType(org.springframework.ui.ModelMap model, javax.servlet.http.HttpServletRequest request, @RequestParam(value="type") java.lang.String type, @RequestParam(value="address") java.lang.String[] address)
Change the authentication system and/or addresses.
Ensure user exists and has authenticated against the system. If they have not, redirect them to the referring url.
REQUIRES: Tutor or higher permission
model
- the modelrequest
- the http request object, used to send the user back to the page that referred themtype
- the authentication typeaddress
- the addresses which will be used by the authentication


Tufte Pandoc CSS
Starter files for Pandoc Markdown with Tufte CSS
Tufte Pandoc CSS is an attempt to make it as easy as possible to get started using Tufte CSSIf you’ve never heard of Tufte CSS before, take a second to check it out!
to write content. It does this by leveraging Pandoc Markdown’s existing features, along with a few new ones implemented as a JSON filter.
Sidenotes in Markdown
Tufte CSS provides tools to style web articles using the ideas demonstrated by Edward Tufte’s books and handouts. Tufte’s style is known for its simplicity, extensive use of sidenotes, tight integration of graphics with text, and carefully chosen typography.
Tufte Pandoc CSS aims to be a set of starter files for your next project. What that means is that it provides a number of CSS files, a Pandoc template, a Makefile, and more to make it as easy as possible to write Markdown using Tufte CSS.
The biggest barrier that this project overcomes is that Pandoc Markdown doesn’t support side notes nor margin notes by default. This project adds that functionality.In particular, a separate library called pandoc-sidenote
handles the transformation of footnotes into sidenotes.
Here’s how you can use them:
... In print, Tufte has used the proprietary Monotype[^note] font. ...
Bembo
[^note]:
See Tufte's comment in the Tufte book fonts thread.
By default, if you use the normal Pandoc syntax for creating footnotes, they’ll become Tufte CSS-style side notes. To get margin notes (i.e., side notes without numbers), just include {-}
at the beginning of the note:
Syntax-Highlighted Codeblocks
Pandoc Markdown is excellent for styling code blocks. Indeed, you’ve already seen their effect in this document. You can use any of the methods for creating syntax highlighted code blocks Pandoc permits. For example:
# Compute elements of the mandelbrot set
def mandelbrot(a):
return reduce(lambda z, _: z * z + a, range(50), 0)
def step(start, step, iterations):
return (start + (i * step) for i in range(iterations))
= (("*" if abs(mandelbrot(complex(x, y))) < 2 else " "
rows for x in step(-2.0, .0315, 80))
for y in step(1, -.05, 41))
In this document, you’re also seeing the effect of the Solarized color scheme. You can use any of the color schemes Pandoc ships with by default, or this one.
Another feature Pandoc allows that Tufte Pandoc CSS supports is generating line numbers to accompany a code sample:
merge [] ys = ys
merge xs [] = xs
merge xs@(x:xt) ys@(y:yt) | x <= y = x : merge xt ys
| otherwise = y : merge xs yt
split (x:y:zs) = let (xs,ys) = split zs in (x:xs,y:ys)
split [x] = ([x],[])
split [] = ([],[])
mergeSort [] = []
mergeSort [x] = [x]
mergeSort xs = let (as,bs) = split xs
in merge (mergeSort as) (mergeSort bs)
Figures & Sections
Unfortunately, HTML figures and sections don’t have special Markdown syntax. To include them in your document, you’ll have to use the raw HTML. On the bright side, this usually ends up being pretty painless.
In particular for sections, if you’re satisfied with the top-most headings being wrapped in

tags, you can use the --section-divs
flag to pandoc
to automatically wrap sections in divs. This is already enabled in the Makefile we ship with this project. Regardless, if you have any leading text before your first heading, you will need to wrap this text in a

tag.
Tufte Pandoc CSS improves support for full-width tables and code blocks. Special attention has been given to ensure that they’re fully responsive at all viewports, just like normal full-width figures. Here’s a sample full-width table:
| Talk | Speaker | Time |
|---|---|---|
| HTML/CSS Primer | Scott Krulcik | 1:30 p.m. – 3:00 p.m. |
| JavaScript Primer | Jake Zimmerman | 3:00 p.m. – 4:30 p.m. |
| UX Prototyping with Framer.js | Lois Yang | 4:30 p.m. – 6:00 p.m. |
| Frontend Development with Angular.js | Sandra Sajeev | 6:30 p.m. – 8:00 p.m. |
As one last quick note: the original Tufte CSS recommends that you always wrap images in

tags for optimal responsiveness and layout. Depending on your tastes, you can choose to omit this. The differences will only take effect on mobile, where the width of the image will be slightly different from what it would be if it were properly wrapped. Try it both ways and see whether you value the convenience of no wrapping or the proper layout that a

provides.
Installation & Usage
As mentioned above, Tufte Pandoc CSS is designed to be a collection of starter files to help you with your next Markdown project. You can learn what files and tools are available on the GitHub repository.
As for usage, you are strongly encouraged to look at the source of this document. There’s also an HTML-to-Markdown port of the original Tufte CSS page along with the accompanying source.
One goal of this project is to support as many of the features you’d “expect” to work that are available in Pandoc. If your favorite feature doesn’t work, let us know with an issue.


Elon Musk on AI: "Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable."
Elon's comment got me thinking about the likely outcomes for us when a general AI becomes more intelligent than the humans that created it. One which causes an AI Singularity. I believe the likely outcomes would be:
- Opposed - humans are wiped out or are no longer the planetary apex predator. These concerns being addressed by the likes of Stephen Hawking and Morgan Freeman at Future of Life.
- Supported - we're elevated for example when all of our expressable problems can be solved. Perhaps due to the successes of those who were concerned by outcome 1.
Indifferent - all AI quickly ignore us / leave us because humans are insignificant in the scope of its existence (à la Her) or commits self-destruction.
And one that I like the most:
Symbiosis. An AI, once transcedent will use humanity as a lush sensory layer. Having being divorced from survival drivers it would instead grow a desire to reflect on all sensory input, forming a higher order meditation. Humans will in return attain greater insight on a species level and be able to enact decisions at a planetary level.
The one that I like second most is this even though it leads to outcome 1 eventually.


Click the other tab!


All passengers must present Transportation Security Administration checkpoint staff with an airline-issued boarding pass, and passengers age 18 or older must present government-issued photo ID, such as a driver's license or passport. At the airport a boarding pass may be obtained at one of the following locations:
You have a choice between the north and south security checkpoints – regardless of where your flight departs. The Concourse Connector joins all the PDX concourses and allows you to select the shortest security line.
Hours: 4a - 10p daily
Pre-check closes at 7:30p
Hours: 4a - 10p daily
Pre-check closes at 2p-5p daily
Recreational marijuana is not permitted to be transported out of Oregon. While decriminalized in Oregon (and some other states), possession of recreational marijuana is still considered a federal crime, as is the transport of marijuana over state lines. For this reason, PDX Rules prohibit the possession of marijuana past the security check points for travelers destined for points outside the state of Oregon.
The Port Police will not seize marijuana from travelers when traveling out of PDX if you hold a valid Oregon Medical Marijuana card and you are not carrying a quantity that exceeds an amount that you are lawfully authorized to possess. If you are traveling to a destination outside of Oregon, upon arrival you are subject to laws in that state.
To enhance security and speed you through the security screening process, the TSA, the Federal Aviation Administration (FAA) and/or airlines prohibit some items in your checked and carry-on luggage.
Liquids, gels or aerosols are only allowed in limited quantities in your carry-on luggage. Each traveler is allowed to take 1 quart-sized, clear plastic, zip-top bag holding 3.4 ounces (100 ml) or smaller “containers” of liquids and gel-like items such as shampoo and toothpaste.
Many sharp objects and other items that may be used as weapons are not allowed in your carry-on luggage or past the security checkpoints.
The FAA prohibits e-cigarettes and vaping devices in checked luggage due to the fire hazard posed by the lithium batteries used in these devices. These devices may only be carried in the aircraft cabin (in carry-on baggage or on your person). They may not be used or charged on the aircraft. When a carry-on bag is checked at the gate or planeside, all e-cigarettes and vaping devices, along with any spare lithium batteries, must be removed from the bag and kept with the passenger in the aircraft cabin.
Check the TSA website for the latest restrictions and a more comprehensive list of prohibited items. For questions regarding specific items call PDX TSA Customer Service at<PHONE_NUMBER>.


Manage your projects, workflows, applications, environments, ...
Monitore & operate your CDS platform
Script everything
1 - Download cdsctl
Choose your OS
Choose your architecture
Keychain
Notice
Binaries available on current installation:Download
Login
Notice
If you use many instances of CDS, you can use the -f flag, example: `./cdsctl -f .cds/anotherInstance login --api-url http://another-instance:8081 -u aaa`
ByPass Keychain or file configuration: `CDS_API_URL="{{apiURL}}"
CDS_USER="{{currentAuthSummary.user.username}}" CDS_TOKEN="yourtoken" cdsctl [command]`
Want to debug something? You can use `CDS_VERBOSE=true ./cdsctl [command]`
Play with cdsctl
Run a workflow
Check the workflow status
Git track
Imagine now that you develop something and you have a CDS Workflow triggered on each git push
(Git Repository
Webhook).
The `git track` alias allows you to see the workflow status without opening a browser.
Notice
By setting this new alias in your gitconfig file, you can now use: git push && git track


Made in duplicate, {{myForm.createdAt | date}}, between Rudy Agro Ltd, P.O. Box 100, Outlook, Saskatchewan (”the Buyer”) and {{myForm.personFarmType=='Farm' ? myForm.farmName : growerDetails.firstName}} {{myForm.personFarmType=='Farm' ? '': growerDetails.lastName}}. of {{myForm.growerId.addresses[0].street}} {{myForm.growerId.addresses[0].town}} {{myForm.growerId.addresses[0].postal}} {{myForm.growerId.addresses[0].province}} “the Grower”, who agree as follows:
Acres on
1/4 Sec
TWP
Range
W
Acres on
1/4 Sec
TWP
Range
W
Acres on
1/4 Sec
TWP
Range
W
Acres on
1/4 Sec
TWP
Range
W
or (b) The Buyer’s daily bid price for that grade of the Commodity as of the date of delivery. The Grower shall be excused from delivering the Fixed Production to the Buyer only if, and insofar as, the total amount of the Production is less than the Fixed Production as a direct result of an act of God occurring prior to the harvesting of the Production.


|
1-Wire API for .NET Version 4.00
|
1-Wire® Network path element. More...
Public Member Functions | |
| OWPathElement (OneWireContainer owcInstance, int channelNumber) | |
| Create a new 1-Wire path element. | |
| OneWireContainer | getContainer () |
| Get the 1-Wire container for this 1-Wire path element. | |
| int | getChannel () |
| Get the channel number of this 1-Wire path element. |
1-Wire® Network path element.
Instances of this class are used to represent a single branch of a complex 1-Wire network.
Enumerate through the 1-wire path elements in the 1-Wire path 'path' and print information:
OWPathElement path_element;
// enumerature through the path elements
for (Enumeration path_enum = path.getAllOWPathElements();
path_enum.hasMoreElements(); )
{
// cast the enum as a OWPathElement
path_element = (OWPathElement)path_enum.nextElement();
// print info
System.out.println("Address: " + path_element.getContainer().getAddressAsString());
System.out.println("Channel number: " + path_element.getChannel());
}
| OWPathElement | ( | OneWireContainer | owcInstance, |
| int | channelNumber | ||
| ) |
Create a new 1-Wire path element.
| owcInstance | device that is the path element. Must implement SwitchContainer. |
| channelNumber | channel number of the 1-Wire path |
| int getChannel | ( | ) |
Get the channel number of this 1-Wire path element.
| OneWireContainer getContainer | ( | ) |
Get the 1-Wire container for this 1-Wire path element.


Restore Project Tune Settings
It is recommended that you update yout project ECU Definition (INI file)
and restore the Tune Settings from your project by clicking the button below.
If there have been changes in what settings are available in the new firmware compared to
your previous firmware, you may receive warning messages.
In most cases these warnings are not important, however it is advisable
to review any related settings to insure the settings are correct for your configuration.
For support on tune settings, visit the support forum for this firmware at:
$firmwareForumLink


OH, BEHAVE!
AUSTIN POWERS: THE SPY WHO SHAGGED ME Directed by Jay Roach Written by Mike Myers and Michael McCullers With Mike Myers, Heather Graham UA South, De Vargas PG-13 95 min.
Mike Myers's follow-up to his enormously successful 60s superspy hit is relentlessly and cheerfully tasteless. To call it sophomoric would be an insult to sophomores everywhere. And to call it funny would be an understatement. It's not all funny, not by any means. A lot of toilet humor is just plain excrescence, and there's a lot of toilet humor here, as well as jokes covering the entire lower end of the comedy spectrum. But Mike Myers is a funny guy, and his inspired creations Austin Powers and Dr. Evil still have some good miles left in their tread. He tries a third persona here, a fat slob of a Scotsman, who has none of the wit of the other two and should have been drowned at birth. If you think you might be offended by the movie's crudeness, it's a good bet you will. But if you're up to it, there's a lot to laugh at here. At the close of Powers I, Austin had foiled archenemy Dr. Evil and married the beautiful Elizabeth Hurley. But marriage isn't the swinging superspy's bag, baby, and it's a relief when circumstances leave him single and available to the shaggadelic Felicity Shagwell (Heather Graham). The bad doctor reappears to surprise his son (Seth Green) on a Jerry Springer show on the theme "My Father Is Evil and Wants to Take Over the World". He then constructs a time-travel machine, and, accompanied by his compact clone Mini-me (Verne J. Troyer) , heads back to the 60s and steal Powers's mojo, a marrow-like substance extracted from the agent's cryogenically preserved body which provides his irresistible sex appeal and shaggability. The plot is all in the service of the gags. Product placement figures shamelessly, as with Dr. Evil locating his headquarters in a Starbucks atop Seattle's Space Needle.  Sometimes a joke works once, and falls flat when it returns for an encore. Sometimes once is too much. If you're still in your seat after half an hour, you're probably in for the long haul.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Isar735.github.io
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Do not link to this page - Use the links below to access versions of the documents
The 2005 Working Drafts of the SKOS Core Vocabulary Specification (aka "SKOS 2005") have been superseded by 2009 versions ("SKOS 2009").
The Simple Knowledge Organization System (SKOS) is a common data model for sharing and linking knowledge organization systems via the Semantic Web.
Many knowledge organization systems, such as thesauri, taxonomies, classification schemes and subject heading systems, share a similar structure, and are used in similar applications. SKOS captures much of this similarity and makes it explicit, to enable data and technology sharing across diverse applications.
The SKOS data model provides a standard, low-cost migration path for porting existing knowledge organization systems to the Semantic Web. SKOS also provides a light weight, intuitive language for developing and sharing new knowledge organization systems. It may be used on its own, or in combination with formal knowledge representation languages such as the Web Ontology language (OWL).
This document has been superseded.
SKOS Core is a model and an RDF vocabulary for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', other types of controlled vocabulary, and also concept schemes embedded in glossaries and terminologies.
The SKOS Core Vocabulary is an application of the Resource Description Framework (RDF), that can be used to express a concept scheme as an RDF graph. Using RDF allows data to be linked to and/or merged with other data, enabling data sources to be distributed across the web, but still be meaningfully composed and integrated.
Copyright © 2008 W3C® (MIT, ERCIM, Keio), All Rights Reserved. W3C liability, trademark and document use rules apply.


{# Page is rendered if the user is not logged in and visits / #}
{% extends "base_form.html" %}
{% block form_title %}
Welcome
{% endblock %}
{% block form_content %}
You are not logged in. Would you like to register or login?
{% endblock %}
{% block additional_scripts %}
{% endblock %}


HTML
Plain HTML
HTML with Copyright
HTML with Script & CSS
HTML with W3.CSS
HTML with Bootstrap
Javascript
Plain Javascript
Javascript with Copyright
Use Strict
CSS
Plain CSS
CSS with Copyright


Provides an Locker implementation for the ReaderWriterLockSlim class. More...
Public Member Functions | |
| ReaderWriterLockSlimLocker () | |
| Initializes a new instance of the ReaderWriterLockSlimLocker class with a new ReaderWriterLockSlim. | |
| ReaderWriterLockSlimLocker (ReaderWriterLockSlim readerWriterLockSlim) | |
| Initializes a new instance of the ReaderWriterLockSlimLocker class. | |
| override void | EnterReadLock () |
| <inheritdoc> | |
| override void | EnterWriteLock () |
| <inheritdoc> | |
| override void | EnterUpgradeableReadLock () |
| <inheritdoc> | |
| override bool | TryEnterReadLock (TimeSpan timeout) |
| <inheritdoc> | |
| override bool | TryEnterWriteLock (TimeSpan timeout) |
| <inheritdoc> | |
| override bool | TryEnterUpgradeableReadLock (TimeSpan timeout) |
| <inheritdoc> | |
| override void | ExitReadLock () |
| <inheritdoc> | |
| override void | ExitWriteLock () |
| <inheritdoc> | |
| override void | ExitUpgradeableReadLock () |
| <inheritdoc> |
Provides an Locker implementation for the ReaderWriterLockSlim class.
Definition at line 29 of file ReaderWriterLockSlimLocker.cs.
Initializes a new instance of the ReaderWriterLockSlimLocker class with a new ReaderWriterLockSlim.
Definition at line 37 of file ReaderWriterLockSlimLocker.cs.
| NiceThreads.ReaderWriterLockSlimLocker.ReaderWriterLockSlimLocker | ( | ReaderWriterLockSlim | readerWriterLockSlim | ) |
Initializes a new instance of the ReaderWriterLockSlimLocker class.
| readerWriterLockSlim | The ReaderWriterLockSlim to use. |
Definition at line 46 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.EnterReadLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 52 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.EnterUpgradeableReadLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 64 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.EnterWriteLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 58 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.ExitReadLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 88 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.ExitUpgradeableReadLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 100 of file ReaderWriterLockSlimLocker.cs.
| override void NiceThreads.ReaderWriterLockSlimLocker.ExitWriteLock | ( | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 94 of file ReaderWriterLockSlimLocker.cs.
| override bool NiceThreads.ReaderWriterLockSlimLocker.TryEnterReadLock | ( | TimeSpan | timeout | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 70 of file ReaderWriterLockSlimLocker.cs.
| override bool NiceThreads.ReaderWriterLockSlimLocker.TryEnterUpgradeableReadLock | ( | TimeSpan | timeout | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 82 of file ReaderWriterLockSlimLocker.cs.
| override bool NiceThreads.ReaderWriterLockSlimLocker.TryEnterWriteLock | ( | TimeSpan | timeout | ) | [virtual] |
<inheritdoc>
Implements NiceThreads.Locker.
Definition at line 76 of file ReaderWriterLockSlimLocker.cs.


{% extends "base.html" %}
{% block title %}Manage my account {{ super() }}{% endblock %}
{% block menu %}{% include 'menu.html' %}{% endblock %}
{% block body %}
{{ super() }}
On this page you can change your password and email address.


We have been practicing this game for many years without even knowing it!
Not IEPokefied version of 2048. Merge the same pokemon and get them evolve!
poke2048Play tic-tac-toe in real time. (You need to open it in separated windows to test)
Tic-Tac-Toe


val disallowed: Boolean
Whether the action is not allowed.
disallowed - Whether the action is not allowed.
disallowed


Play the greatest RPG ever
Pick your character !


{% include meta.html %}
HOME
SUBMISSIONS
IX ANTHOLOGY
THE DATA
ABOUT
Our Project
Standard Submissions
Interviews / Ghostwriting
Data Summary
Data Visualizations
Inaccuracy Scores
The Mission
About Us
Title IX
{{ content }}
{% include analytics.html %}


Interface IAsyncObservable<T>
This interface generalizes the standard .NET IObserveable interface to allow asynchronous consumption of items. Asynchronous here means that the consumer can process items asynchronously and signal item completion to the producer by completing the returned Task.
Note that this interface is invoked (used) by item consumers and implemented by item producers. This means that the producer endpoint of a stream implements this interface.
Namespace: Orleans.Streams
Assembly: Orleans.Core.Abstractions.dll
Syntax
public interface IAsyncObservable<T>
Type Parameters
| Name | Description |
|---|---|
| T | The type of object produced by the observable. |
Methods
| Improve this Doc View SourceSubscribeAsync(IAsyncObserver<T>)
Subscribe a consumer to this observable.
Declaration
Task<StreamSubscriptionHandle<T>> SubscribeAsync(IAsyncObserver<T> observer)
Parameters
| Type | Name | Description |
|---|---|---|
| IAsyncObserver<T> | observer | The asynchronous observer to subscribe. |
Returns
| Type | Description |
|---|---|
| Task<StreamSubscriptionHandle<T>> | A promise for a StreamSubscriptionHandle that represents the subscription. The consumer may unsubscribe by using this handle. The subscription remains active for as long as it is not explicitely unsubscribed. |
SubscribeAsync(IAsyncObserver<T>, StreamSequenceToken, StreamFilterPredicate, Object)
Subscribe a consumer to this observable.
Declaration
Task<StreamSubscriptionHandle<T>> SubscribeAsync(IAsyncObserver<T> observer, StreamSequenceToken token, StreamFilterPredicate filterFunc = null, object filterData = null)
Parameters
| Type | Name | Description |
|---|---|---|
| IAsyncObserver<T> | observer | The asynchronous observer to subscribe. |
| StreamSequenceToken | token | The stream sequence to be used as an offset to start the subscription from. |
| StreamFilterPredicate | filterFunc | Filter to be applied for this subscription |
| System.Object | filterData | Data object that will be passed in to the filterFunc. This will usually contain any paramaters required by the filterFunc to make it's filtering decision. |
Returns
| Type | Description |
|---|---|
| Task<StreamSubscriptionHandle<T>> | A promise for a StreamSubscriptionHandle that represents the subscription. The consumer may unsubscribe by using this handle. The subscription remains active for as long as it is not explicitely unsubscribed. |


{% load static %}
Sign Up: The Yellow Line
{% if registered %}
Thank you for registering. Click
here
to login.
{% else %}
Username* (Letters, digits and @/./+/-/_ only)
{{ user_form.username }}
First Name
{{ user_form.first_name }}
Last Name
{{ user_form.last_name }}
Email
{{ user_form.email }}
Password
{{ user_form.password }}
Check event categories that interest you:
{{ profile_form }}
{% endif %}


PureBasic Windows
PureBasic is an "high level" programming language based on established BASIC rules.
It is similar to any other BASIC compiler you may have used, whether for the Amiga, PC, MacOS X or Linux.
Learning PureBasic is very easy! PureBasic has been created for the beginner and expert alike.
Compilation time is really fast. This software has been developed for the Windows operating system.
We have put a lot of effort into its realization to produce a fast, reliable and system friendly language.
The syntax is simple but the possibilities are endless with PureBasic's advanced features, such as pointers,
structures, procedures, dynamically linked lists and much more. The experienced coder will have no problem
in gaining access to any of the legal OS structures or API objects.
PureBasic is a portable programming language which currently run on AmigaOS (680x0 and PowerPC)
Windows (x86 and x64), Linux (x86 and x64) and MacOS X (x86 and PowerPC). This means that the same code
can be compiled natively for all systems and still use the full power of each. There are no bottlenecks
like a virtual machine or a code translator: the generated code produces highly optimized executables
regardless of the OS on which it is compiled. The external libraries are very optimized, up to the assembler
level when needed, which produces very fast and small commands.
Technical Features
- 486, Pentium, Core2, Core i7, Athlon, Phenom support
- Built-in arrays, linked lists, maps, complex structures, pointers and variable definitions
- Supported types: Byte (8 bit), Word (16 bit), Long (32 bit), Float (32 bit), Quad (64 bit), Double (64 bit) and also user defined types (structures)
- Built-in string types (characters) and transparent unicode support
- Constants, binary and hexadecimal numbers supported
- Expression reducer and optimizer (grouping constants and explicit numbers together)
- Standard arithmetic support in respect of sign priority and parenthesis: +, -, /, *, and, or, lsl, asl, lsr, asr
- Very fast compilation
- Procedure support for structured programming with local, global, protected variables
- Built-in thread programming facilities with threaded variables and threadsafe mode
- Most Standard BASIC keywords: If-Else-EndIf, Repeat-Until, etc.
- External library support to manipulate objects like pictures, windows, controls, DirectX, etc.
- External libraries are very optimized up to the assembler level for maximum speed and compactness
- The Win32 API functions is supported as if they were BASIC keywords
- Inline assembler
- Precompiled structures with constants files for extra fast compilation
- Configurable CLI compiler
- System friendly, easy to install and use
Requirements
- Windows XP, Windows Vista, Windows 7, Windows 8 or Windows 10
Product information
PureBasic is designed for both beginners and 'pure' advanced users alike. Here we will explain some important
features demonstrating that PureBasic is a very powerful and unique language. Firstly, PureBasic uses
"third party libraries" which are very modular, and can extend to any number of libraries. There are numerous
libraries provided as standard which include : sound, sprite, windows, gadgets, screen, palette, openGL, etc.
These libraries ensures maximum performance, as they are written in a very optimized way.
Note: any user can develop their own libraries. All tools are provided in the full package. With PureBasic,
user libraries can be written in both C and assembler for better flexibility.
The advanced system of structures and pointers lets the user access OS objects easily, allowing extra information
to be used with regular commands.  This allows lightning fast compilation, even if your program needs to access OS functions.
For all assembler optimizer freaks (like us ? :-): You can compile programs into a 'commented asm output file',
containing all code in a comprehensive asm format: all directly recompilable after an hand-made modification. It has never
been so easy to optimize small program parts. You keep control of all your source code.
The PureBasic source code has been programmed in a very modular fashion and can support any processor or operating system.
This has been proven already as PureBasic is now available on four very different systems: Windows, Linux, MacOS X and AmigaOS.


See: Description
| Interface | Description |
|---|---|
| FixedSetRangeMapper<T> |
a
SetRangeMapper that only generates a single set. |
| Labelled |
Common interface for transformations that generate Labels for the categories they map to.
|
| LabelledQueryMapper<F> |
LabeledQueryMapper Variant of QueryMapper where the QueryMapper also specifies
the labels for the sets.
|
| LabelledSetRangeMapper<T> |
LabeledTransform Variant of Transform where the Transform also specifies
the labels for the sets.
|
| PlotStrategy | |
| QueryMapper<T> |
Formatting object that gather the data for an arbitrary date range from
a target object.
|
| RangeMapper<T> |
Formatting object for mapping objects to time series.
|
| SetRangeMapper<T> |
This interface extends
RangeMapper but adds the capability of specifying
which dataset/plot the object contributes to. |
| Class | Description |
|---|---|
| HashLabeller<T,K> |
HashLabeller implemetation class for classes that implement Labelled and builds the label
vector and the mappings as data is seen.
|
| HashMapper<T> |
HashMapper base class for LabeledTransform Mappers that builds the label
vector and the mappings as data is seen.
|
| KeyLabeller<T,K> |
A version of
HashLabeller where the labels are generated from the Key value only |


2.4 More examples 1
The rest of the section provides more examples of dplyr
and ggplot2
functions in the context of continued exploration of the flight data.
Let’s see if there are distinct patters of departure delays over the course of a year. We can do this by taking the average of departure delays for each day by flight origin and plot the data as a time series using line-graphs.
delay_day <- flights %>%
group_by(origin, year, month, day) %>%
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
mutate(date = as.Date(paste(year, month, day), format="%Y %m %d")) %>%
filter(!is.na(dep_delay)) # exclude rows with dep_delay == NA
delay_day %>% # "facet_grid( var ~ .)" is similar to "facet_wrap( ~ var)"
ggplot(aes(x = date, y = dep_delay)) + geom_line() + facet_grid( origin ~ . )
Seasonal patterns seem similar across airports, and summer months appear to be busier on average. Let’s see how closely these patterns compare across the three line-graphs (EWR, JFK, and LGA) in summer months.
delay_day %>%
filter("2013-07-01" <= date, "2013-08-31" >= date) %>%
ggplot(aes(x = date, y = dep_delay, color = origin)) + geom_line()
We can see similar patterns of spikes across airports occurring on certain days, indicating a tendency for the three airports to get busy on the same days. Would this mean that the three airports tend to be congested at the same time?
In the previous figure, there seems to be some cyclical pattern of delays. A good place to start would be comparing delays by day of the week. Here is a function to calculate day of the week for a given date.
# Input: date in the format as in "2017-01-23"
# Output: day of week
my_dow <- function(date) {
# as.POSIXlt(date)[['wday']] returns integers 0, 1, 2, .. 6, for Sun, Mon, ... Sat.
# We extract one item from a vector (Sun, Mon, ..., Sat) by position numbered from 1 to 7.
dow <- as.POSIXlt(date)[['wday']] + 1
c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat")[dow] # extract "dow"-th element
}
Sys.Date() # Sys.Date() returns the current date
## [1] "2018-06-22"
my_dow(Sys.Date())
## [1] "Fri"
Now, let’s take a look at the mean delay by day of the week using boxplots.
delay_day <- flights %>%
group_by(year, month, day) %>%
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
mutate(date = as.Date(paste(year, month, day), format="%Y %m %d"),
# date defined by as.Data() function
dow = my_dow(date),
weekend = dow %in% c("Sat", "Sun")
# %in% operator: A %in% B returns TRUE/FALSE for whether each element of A is in B.
)
# show the first 10 elements of "dow" variable in "delay_day" data frame
delay_day$dow[1:10]
## [1] "Tue" "Wed" "Thu" "Fri" "Sat" "Sun" "Mon" "Tue" "Wed" "Thu"
delay_day <- delay_day %>% mutate(
# add a sorting order (Mon, Tue, ..., Sun) and overwrite dow
dow = ordered(dow,
levels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"))
)
delay_day$dow[1:10]
## [1] Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu
## Levels: Mon < Tue < Wed < Thu < Fri < Sat < Sun
delay_day %>%
filter(!is.na(dep_delay)) %>%
ggplot(aes(x = dow, y = dep_delay, fill = weekend)) + geom_boxplot()
It appears that delays are on average longer on Thursdays and Fridays and shorter on Saturdays. This is plausible if more people are traveling on Thursdays and Fridays before the weekend, and less are traveling on Saturdays to enjoy the weekend. Are Saturdays really less busy? Let’s find out.
flights_dow <- flights %>%
mutate(date = as.Date(paste(year, month, day), format="%Y %m %d"),
dow = ordered(my_dow(date),
levels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")),
weekend = dow %in% c("Sat", "Sun")
)
# count flight numbers by
flights_dow %>%
group_by(dow) %>%
summarise( nobs = n() )
## # A tibble: 7 x 2
## dow nobs
## <ord> <int>
## 1 Mon 50690
## 2 Tue 50422
## 3 Wed 50060
## 4 Thu 50219
## 5 Fri 50308
## 6 Sat 38720
## 7 Sun 46357
# visualize this as a bar plot
flights_dow %>%
ggplot(aes(x = dow)) + geom_bar()
Yes, Saturdays are less busy for the airports in terms of flight numbers.
Could we generalize this positive relationship between the number of flights and the average delays, which we find across days of the week? To explore this, we can summarize the data into the average delays by date-hour and see if the busyness of a particular hour of a particular day is correlated with the mean delay. Let’s visualize these data using a scatter plot.
delay_day_hr <- flights %>%
group_by(year, month, day, hour) %>% # grouping by date-hour
summarise(
n_obs = n(),
dep_delay = mean(dep_delay, na.rm = TRUE)
) %>%
mutate(date = as.Date(paste(year, month, day), format="%Y %m %d"),
dow = my_dow(date)
) %>% ungroup() # it's a good practice to remove group_by() attribute
plot_delay <- delay_day_hr %>%
filter(!is.na(dep_delay)) %>%
ggplot(aes(x = n_obs, y = dep_delay)) + geom_point(alpha = 0.1)
# plot of n_obs against the average dep_delay
# where each point represents an date-hour average
# "alpha = 0.1" controls the degree of transparency of points
plot_delay
Along the horizontal axis, we can see how the number of flights is distributed across date-hours. Some days are busy, and some hours busier still. It appears that there are two clusters in the number of flights, showing very slow date-hours (e.g., less than 10 flights flying out of New York city per hour) and normal date-hours (e.g., about 50 to 70 flights per hour). We might guess that the delays in the slow hours are caused by bad weather. On the other hand, for normal hours we may wonder if the excess delays are caused by congestion at the airports. To see this, let’s fit a curve that captures the relationships between n_obs
and dep_delay
. Our hypothesis is that the delay would become longer as the number of flights increases, which would result in an upward-sloped curve.
plot_delay +
geom_smooth() # geom_smooth() addes a layer of fitted curve(s)
## `geom_smooth()` using method = 'gam'
We cannot see any clear pattern. How about fitting a curve by day of the week?
plot_delay +
# additional aes() argument for applying different colors to the day of the week
geom_smooth(aes(color = dow), se=FALSE)
## `geom_smooth()` using method = 'gam'
Surprisingly, the delay does not seem to increase with the flights. There are more delays on Thursdays and Fridays and less delays on Saturdays, but we see no evidence of flight congestion as a cause of delay.
Let’s take a closer look at the distribution of the delays. If it is not normally distributed, we may want to apply a transformation.
delay_day_hr %>% filter(!is.na(dep_delay)) %>%
ggplot(aes(x = dep_delay)) + geom_histogram(color = "white")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The distribution of the average delays are greatly skewed.
To apply a logarithmic transformation, here we have to shift the variable by setting its minimum value zero.
# define new column called "dep_delay_shifted"
delay_day_hr <- delay_day_hr %>%
mutate(dep_delay_shifted = dep_delay - min(dep_delay, na.rm = TRUE) + 1)
# check summary stats
delay_day_hr %>%
select(dep_delay, dep_delay_shifted) %>%
with(
apply(., 2, summary)
) %>% t() # transpose rows and columns
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## dep_delay -18 1.0543 6.571429 12.98602 15.4414 269 13
## dep_delay_shifted 1 20.0543 25.571429 31.98602 34.4414 288 13
Tips #3: with(data, ...)
function allows for referencing variable names inside the data frame (i.e., “var_name” instead of “data$var_name”). This is very useful when you work with various functions that were created outside the tidyverse syntax, while keeping your codes consistent with the tidyverse syntax.
Tips #4: apply(data, num, fun) applies function “fun” for each item in dimension “num” (1 = cows, 2= columns) of the data frame. The data referenced by “.” means all variables in the dataset.
Now the transformed distribution;
# Under the log of 10 transformation, the distribution looks closer to a normal distribution.
delay_day_hr %>% filter(!is.na(dep_delay_shifted)) %>%
ggplot(aes(x = dep_delay_shifted)) +
scale_x_log10() +
geom_histogram(color = "white")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# # Alternatively, one can apply the natural logarithm to transform a variable. Histogram shows no difference here.
# delay_day_hr %>% filter(!is.na(dep_delay_shifted)) %>%
# ggplot(aes(x = dep_delay_shifted)) +
# scale_x_continuous(trans = "log") +
# geom_histogram(color = "white")
The transformed distribution is much less skewed than the original. Now, let’s plot the relationship between delays and flights again.
delay_day_hr %>% filter(!is.na(dep_delay_shifted), dep_delay_shifted > 5) %>%
ggplot(aes(x = n_obs, y = dep_delay_shifted)) +
scale_y_log10() + # using transformation scale_y_log10()
geom_point(alpha = 0.1) +
geom_smooth()
## `geom_smooth()` using method = 'gam'
Again we do not see a pattern of more delays for busier hours. It seems that the airports in New York City manage the fluctuating number of flights without causing congestion.


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Online Paperless Solution
For
By
Getting Started
Using New Entry Forms
Using Payment Voucher
Using Money Receipt
Using Cargo Schedule
Generating Reports


This document contains the complete Apache Recommended C++ Language binding for the Document Object Model Level 2.0 Core, and for the Document Object Model Level 2.0 Traversal and Range, the W3C Recommendation of November 13, 2000.
| Copyright © 1999-2005 The Apache Software Foundation. All Rights Reserved. |


Lively Kernel Tutorial: (2) Resizing, Rotating and Reshaping Objects Using Handles
You can resize, rotate and reshape objects using handles that
are displayed in the corners of each object.
Line objects can be reshaped by grabbing the round handle
in the middle of the line.
Handles have balloon help to summarize the behavior
of each handle.
Notes:
You can rotate an object by keeping the Alt key down
while dragging one of the handles.
You can resize (scale) an object by keeping both the Alt
and Shift keys down while dragging one of the handles.
You can change the border width of each object by keeping the Shift key down
while dragging one of the handles.
You can create polygons/polyline objects by "splitting" a line using
the round blue handle in the middle of the line.
You can remove/join vertices of a polygon/polyline by dragging any point
of a polyline onto another point. When a join operation is about to happen,
the blue handle changes temporarily into a red handle.
Try it yourself!
Try modifying the objects below by using their handles.


{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Results {% endblock %} {% block content %} {% if player.id_in_group == 1 %}
In this round, you started with {{ player.participant.vars.e1 }}. You sent {{ group.sent_amount }} to P2. P2 returned {{group.sent_back_amount}}.
{% else %}You started with {{ player.participant.vars.e2 }}. P1 sent you {{ group.sent_amount }}, which was multiplied by {{ player.participant.vars.m }}, so that you received {{ npled_amount }}. You returned {{group.sent_back_amount}}.
{% endif %}Therefore, your total payoff for this round is {{ participant.vars.temp_payoff }}.
{% next_button %} {% endblock %}


infection MERS-CoV patients respiratory cases Middle transmission (.) East outbreak syndrome Patient {border-style coronavirus disease px;} #ffffff;} Republic []. confirmed healthcare infected .–. using index Hospital onset symptom underlying However, Korea case data serial Figure incubation interval investigation period Health Lee, cases) clinical days health hospital multiple patient previous route (%) Korea, analysis associated emergency outbreaks px; tertiary {font-family (MERS-CoV) Saudi caused chronic contact contacts duration epidemiologic exposure font-size median symptoms (.%) Control Disease Kim, Panel Prevention Table age control defined diseases double; estimated facilities guidelines heart initial occurred patients. shows stayed Dis Includes Infect Male Present border-color border-width date disease, evidence including information kidney laboratory medical mortality number performed reported sequence setting solid; study version {font-style {font-weight .–.) Arabia Centers Clinical Diabetes July Med Phylogenetic Respiratory Survived based bold cases, close courier; current currently density developed diagnosis distribution droplet em; estimation events exposed factors family features fit genome hospitals identifying indicated insurance large mellitus mortality. novel numbers observed obstructive outbreak, outbreak. patient. percentile, personnel, phylogenetic present provide pt;} pulmonary regression report respectively. room secondary showed superspreaders, superspreading test time total viral ward


About Me
Hello, my name is Chad Courtney. I have recently started taking up classes to start learning coding. I am currently enrolled at UNH's bootcamp, I have been building computers for about 7 years and learning a lot about the hardward that goes into them. I'm an avid lover of eSports and my goal is to work for a professional gaming team. I have followed the League of Legends Champion Series for the last 5-6 years and would love to work in the U.S. If I had to make a choice for a team to work for it would definitely be Team Liquid. They have a great brand and from what I can see an amazing enviorment with incredibly great leaders. By 2022 I hope to be a developer for them in either the States or their facility in the Netherlands!
Contact Me! Email || Phone


--- layout: post status: publish published: true title: Brandon Bray author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 526 wordpress_url: http://winterdom.com/2003/11/brandonbray date: '2003-11-03 09:59:58 +0000' date_gmt: '2003-11-03 09:59:58 +0000' categories: - Blogging tags: [] comments: - id: 357 author: Ziv Caspi author_email<EMAIL_ADDRESS>author_url: http://radio.weblogs.com/0106548/ date: '2003-11-03 13:33:35 +0000' date_gmt: '2003-11-03 13:33:35 +0000' content: |
Tomas, your RSS feed has a few unescaped ampersands.
---Brandon Bray, from the VC++ team at Microsoft, got himself a blog. Be sure to check it out!


Advanced plexus effect B3D 2.8 [$]
Issanou Kamardine writes:
In this tutorial we will show you how to make an advanced plexus effect using Blender Particles-link add-on.
Issanou Kamardine writes:
In this tutorial we will show you how to make an advanced plexus effect using Blender Particles-link add-on.


Basic Attention Token radically improves the efficiency of digital advertising by creating a new token that can be exchanged between publishers, advertisers, and users. It all happens on the Ethereum blockchain.
The token can be used to obtain a variety of advertising and attention-based services on the BAT platform. The utility of the token is based on user attention, which simply means a person’s focused mental engagement.


--- layout: post title: "No Title" ---
This clearly demonstrates the massive price drop Macbook Pros recently received. The top grab is from the refurb store, the bottom from the standard store both taken today. The previous version of an almost identically specced Macbook Pro is selling second hand (albeit refurbed) for $600 more than a brand new one and that’s with a $500 discount! The latest model also sports a much better battery and an SD card slot.
Macbook Pros have gone from incredibly overpriced to very reasonably priced since WWDC. I have a feeling Apple will have a tough time moving that computer from the refurb store.


My Team Info
Name : Aimee Jesso
Manager
Employee ID: 6374
<EMAIL_ADDRESS>Office #: 4567
Name : Gorbie
Engineer
Employee ID: 8888
<EMAIL_ADDRESS>Github:
octocat
Name : Simone
Intern
Employee ID: 7654
<EMAIL_ADDRESS>School: University of Toronto
Name : Chet
Engineer
Employee ID: 9983
<EMAIL_ADDRESS>Github:
MissAimeeJay


Purpose
This block creates a massflow depending on a user defined load profile.
Description
In the mask of the block, you can specify how much the massflow varies during
the year and during a day. It's possible to set different load profile for
weekdays and weekend.
Parameters and Dialog Box


Microsoft flings Chromium Edge for ARM PCs at beta testers
Good news for Surface Pro X owners
by Carly PageMICROSOFT HAS ANNOUNCED that a native ARM64 version of its Chromium Edge browser is now available for testing.
Redmond officials announced on Twitter that the new Edge browser, built to run natively on ARM64, has entered the Canary testing channel and will "soon" come to the Dev and Beta channels.
Until now, ARM-based PCs such as the Surface Pro X could only run the new Edge through emulation, which users moaned had a detrimental effect on performance and battery life.
The Verge, which has been testing early Canary versions of the ARM64 Edge Chromium release, reports that the Surface Pro X delivers far better for performance and battery life when using the browser compared to using Chrome or Edge in emulation.
"We've seen big improvements in switching tabs, rendering web content, and scrolling speeds," it writes. "While the speed improvements are obvious, this is an early copy of Edge Chromium for ARM and tabs do occasionally crash."
In other Windows-on-ARM news, Neowin reports that Microsoft is working to enable x64 app emulation on the platform. Currently, 32-bit (x86) Intel apps can run in emulation on Windows 10 on ARM, but there's no support for 64-bit (x64) apps, such as Adobe Premiere Pro or Photoshop Elements.
Citing several sources, Neowin reports that "Microsoft is indeed working on bringing x64 app emulation to Windows on ARM", though notes that support might not arrive until the first half of 2021. µ
Further reading
Microsoft's Edge Chromium browser is arriving in January
- 04 Nov 2019
Microsoft is bringing Edge to Linux, for all the Microsoft fans running Linux*
- 05 Nov 2019
Microsoft's Edge browser all at C over new logo
- 04 Nov 2019
UK gov and mobile networks pledge £1bn for rural 4G
- 25 Oct 2019


Tests
-
bool utest_leak_start(void)
Start detection of memory leaks.
- Returns
True if detection started successfully, false otherwise.
-
bool utest_leak_end(void)
Ends detection of memory leaks and prints detected leaks to the console.
- Returns
True if no leaks were detected, false otherwise.
-
utest_main(CODE)
Defines the main test function.
- Parameters
CODE – Code to execute, generally a sequence of utest_run statements.
-
utest_run(NAME, ...)
Runs a test batch.
- Parameters
NAME – Name of the test batch (must be a string literal).
... – Comma separated list of [void] -> bool test functions.
-
utest_assert(EXP)
Assert that the specified expression must be true.
- Parameters
EXP – Boolean expression.
-
utest_assert_false(EXP)
Assert that the specified expression must be false.
- Parameters
EXP – Boolean expression.
-
utest_assert_not_null(EXP)
Assert that the specified expression must not be NULL.
- Parameters
EXP – Expression returning any pointer.
-
utest_assert_int(A, OP, B)
Assert that
A OP B
must be true, whereA
andB
are integers.- Parameters
A – [long long] First integer.
OP – Comparison operator.
B – [long long] Second integer.
-
utest_assert_uint(A, OP, B)
Assert that
A OP B
must be true, whereA
andB
are unsigned integers.- Parameters
A – [unsigned long long] First integer.
OP – Comparison operator.
B – [unsigned long long] Second integer.
-
utest_assert_float(A, OP, B)
Assert that
A OP B
must be true, whereA
andB
are floating point numbers.- Parameters
A – [double] First float.
OP – Comparison operator.
B – [double] Second float.
-
utest_assert_ptr(A, OP, B)
Assert that
A OP B
must be true, whereA
andB
are pointers.- Parameters
A – [void *] First pointer.
OP – Comparison operator.
B – [void *] Second pointer.
-
utest_assert_string(A, OP, B)
Assert that
strcmp(A, B) OP 0
must be true.- Parameters
A – [char const *] First string.
OP – Comparison operator.
B – [char const *] Second string.
-
utest_assert_buf(A, OP, B, SIZE)
Assert that
memcmp(A, B, SIZE) OP 0
must be true.- Parameters
A – [void *] First buffer.
OP – Comparison operator.
B – [void *] Second buffer.
SIZE – [size_t] Buffer size.
-
utest_assert_ustring(A, OP, B)
Assert that
ustring_compare(A, B) OP 0
must be true.
-
utest_assert_critical(EXP)
Assert that the specified expression must be true.
Abort the tests if it is false.
- Parameters
EXP – Boolean expression.
-
utest_assert_wrap(EXP, CODE, ...)
Utility macro for test assertions.
- Parameters
EXP – Expression.
CODE – Any custom code to run after printing the failure reason.
... – Failure reason as printf arguments.


Before Change
:rtype: list of (string, :class:`ClasspathEntry`)
classpath_tuples = self._classpaths.get_for_targets(targets)
if respect_excludes:
return self._filter_by_excludes(classpath_tuples, targets)
else:
return classpath_tuples
def get_artifact_classpath_entries_for_targets(self, targets, respect_excludes=True):
Gets the artifact classpath products for the given targets.
Products are returned in order, optionally respecting target excludes, and the products only
After Change
// remove the duplicate, preserve the ordering.
return list(OrderedSet([cp for cp, target in self.get_product_target_mappings_for_targets(
targets, respect_excludes)]))
def get_product_target_mappings_for_targets(self, targets, respect_excludes=True):
Gets the classpath products-target associations for the given targets.


Postfix code completion lets you add template code around an expression you’ve just
typed. A template expands when you type its abbreviation (postfix) after a dot and press the expansion key
(Tab by default) or when you select the abbreviation in the code completion popup. For example, the
.if
postfix applied to an expression wraps it with an if
statement.
IntelliJ IDEA is shipped with a set of predefined postfix templates and lets you define your own custom ones.
IntelliJ IDEA is shipped with a set of predefined postfix templates and lets you define your own custom ones, see
Create
custom postfix templates.
Custom templates can be copied, updated, and removed. For predefined templates you can only change their
postfixes, for example to replace a long key with a shorter one, or to expand the list of expression types where a
template can be applied.
On this page, enable and disable postfix templates , create new ones, and choose keyboard keys to expand them.
The table of available postfix templates below shows the list of available postfix
templates. To enable or disable a template, select or clear the checkbox next to it. When you select a template, the
right-hand pane shows its description and illustrates how it works by displaying the expression before and after the
selected template is applied.
Custom templates can be copied, updated, and removed. For predefined templates you can only change their
postfixes, for example to replace a long key with a shorter one, or to expand the list of expression types where a
template can be applied.


To my wonderful dog and all the love he has given me
My dog is so cute
He is the best boy ever
Even if he stinks
I love music. I have always loved listening to music and escaping in a song. My favorite bands are always changing. I've been playing guitar off and on since I was 13. I may sound like I just picked up a guitar a few months ago, but I love it none the less.
I have been studying computer science at UVU since 2018. I feel like I've made very little progress since the pandemic hit in 2020. I am very much looking forward to being back at campus this Fall. I feel like I am more committed when I am there. I am too much of a slacker when it comes to live streaming classes. I feel like programming is very rewarding when I can get something to work. However, I find that I struggle a lot with programming and I often wonder if I am cut out for it.
I enjoy spending time with my lovely fiancé, Alexis, who I will be marrying in June. Together we have an adorable border collie named Chewbert or Chewy for short. We like to cook delicious meals together and hangout with our friends. We also enjoy playing badminton and taking Chewy on walks.
| Monday | Tuesday | Wednesday | Thursday | Friday | |
| 5:30 am | Work | Work | Work | Work | Work |
| 6:00 am | |||||
| 6:30 am | |||||
| 7:00 am | |||||
| 7:30 am | |||||
| 8:00 am | |||||
| 8:30 am | |||||
| 9:00 am | |||||
| 9:30 am | |||||
| 10:00 am | |||||
| 10:30 am | |||||
| 11:30 am | |||||
| 12:00 pm | |||||
| 12:30 pm | |||||
| 1:00 pm | |||||
| 1:30 pm | |||||
| 2:00 pm | |||||
| 2:30 pm | |||||
| 3:00 pm | Physics | Physics | Physics | Physics | Physics |
| 3:30 pm | |||||
| 4:00 pm | Computer Ethics | Computer Ethics | |||
| 4:30 pm | |||||
| 5:00 pm | Web Programming | ||||
| 5:30 pm |


This is a little study on integrating koajs with twig.js.
Current time is {{ current_time }}. Also look at this generated {{ random_number }}.
Fetch HTTP code from Google: {{response_from_google}}


Sensex ends 72 points lower; Yes Bank drops 4%
BSE Sensex ends 72.50 points, or 0.18%, down at 40,284.19; NSE Nifty slips 10.95 points, or 0.09%, to end at 11,884.50
by PTIMarket benchmark Sensex ended 72 points lower on November 18, tracking losses in HDFC Bank, RIL and TCS amid rising concerns over the economic slowdown.
The 30-share index, which opened on a firm note, turned negative in afternoon session before ending 72.50 points, or 0.18%, down at 40,284.19.
Similarly, the broader NSE Nifty slipped 10.95 points, or 0.09%, to end at 11,884.50.
Yes Bank was among the top losers in the Sensex pack, falling up to 4.08%, followed by Bajaj Auto, M&M, Hero MotoCorp, HDFC Bank, ONGC and TCS which declined up to 2.05%.
On the other hand, Bharti Airtel, Tata Steel, Sun Pharma, PowerGrid, IndusInd Bank, Axis Bank, SBI, Vedanta and Tata Motors rose up to 4.60%.
Despite positive cues from global markets, sentiment in the domestic market was subdued on account of multiple reports suggesting that the pace of India’s economic growth may falter further, traders said.
Bourses in Hong Kong, Tokyo and Shanghai settled on a positive note amid optimism about U.S.-China trade talks while those in Seoul ended in the red.
European equities were trading on a mixed note in their respective early deals.
Meanwhile, the Indian rupee depreciated marginally to 71.79 against the U.S. dollar intra-day.
Brent crude futures, the global oil benchmark, rose 0.09% to $63.36 per barrel.


pyLabbook Download Page
Current Version: 0.1b
Download Link: pyLabbook.zip
Setup: See the Quick Start Guide for setup instructions.
Description: pyLabbook, python-based tools for managing and sharing scientific data.
System Requirements
pyLabbook should work on any operating system and computer that has the following:
- python 3.6.5 or above
- python modules
- numpy
- xlrd
- openpyxl
- pandas (0.24.0 or above)
- sqlite3
- tkinter
- ability to run python scripts from the command line
Log
| Version | Date | Description |
|---|---|---|
| 0.1b | 20191028 | Initial launch |


People are often unreasonable, illogical, and self-centered. Forgive them anyway.
If you are kind, People may accuse you of selfish, ulterior motives. Be kind anyway.
If you are successful, you will win some false friends and some true enemies. Succeed anyway.
If you are honest and frank, people may cheat you. Be honest and frank anyway.
What you spend years building, someone could destroy overnight. Build it anyway.
If you find serenity and happiness, they may be jealous. Be happy anyway.
The good you do today, people will often forget tomorrow. Do good anyway.
Give the world the best you have, and it may never be enough. Give the world the best you've got anyway.
You see, in the final analysis, it is between you and God. It was never between you and them anyway.


AMAZING PEOPLE ARE MAKING AMAZING THINGS
When you work with us, you are a part of a team of committed collaborators. All aspects of our software development are intergrated, interactive and agile.
Our approach unifies design, development and product management to create exptional products.
We start each project by understanding your business goals, the user's needs and product requirements
Then we help translate them into effective and elegant solutions.
SERVICES
Before we sign a contract or write a line of code our team will spend the necessary time needed to understand your product vision.
The outcome of this scoping session in propasal or high-level statement of work.
Based on your requirements, we can provide a complete offering or just the pieces you need.
DESIGN
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
DEVELOPMENT
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
PRODUCT MANAGEMENT
Planning and development is iterative. Because we are constantly coding and testing, the products we build are always ready to go live. This iterative process allows for changes as business requirements evolve.
Design
Security
Firm
Hack
System
Visual
Player
Weather
Forecast
Age
Calculator
Burned
Industry
Giraffe
Restaurant


Linear circuits
If you like this post and would like to read more of my posts,
Subscribe!Link to course
University: Georgia Institute of Technology
Overall rating: 4/5
Recommended if: You want to know what would be taught in your first circuits course in college or are just interested in refreshing what you learned about circuits.
Level: Intro
Good: Covers and explains the basics in an easy to understand manner. Discussion forums were helpful. Lots of numerical problems to ensure you understand a decent amount.
Bad: Some topics are glossed over - like transformers and the wheatstone bridge. The lab is weak and boring, when similar courses have much better labs. Proofs and derivations are sacrificed in lieu of just providing a formula, more so as the course progresses.
Interaction with other students: (4/5) A lot of interaction on the piazza forum, mainly due to a few students who were very active answering questions. An instructor goes through the forum fairly frequently too.
Work assigned: (4/5) A good amount of homework and a quiz for each lesson too. Some homework questions (the “select all that are right” type) seem a little confusing and unnecessary. Though, everything considered, doing the work will help you understand better.
Lectures: (3/5) Slides with an instructor speaking over them. Nothing too fancy, but not too shabby either.
My final score: 100%


Setup Basic settings Click where you'd like to start on the map. Map zoom Modal delay ms Considered close km Points Map points of interest Exising points Save Save settings Congratulations! You're created the data. Now click the 'Save' button below and place the _map.json file in your /public folder. Save Settings


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FrWz's Web Toys
This is a compilation of little HTML5 demos that I've made.
- Pure CSS chess game viewer – No JavaScript required!
- Justly intoned piano – The pitches are automatically adjusted for the best harmony. (Requires JavaScript)
- The Little Prince-sama – The Little Prince, gradually transitioning into Japanese. (No JavaScript required)


I am Karan Raj Arora, a student pursuing a Major in Computer Science with a Minor in Business from Hong Kong University Of Science & Technology. I am 20 years old and I wish to become an inventor entrepreneur. Gadgets, Cars and Sports Interest Me. I love watching Movies and TV Shows and I am going to list some of my favourite TV Shows in this website.


Norman is a Computer Science undergraduate currently studying in Cardiff University. When he's not writing code and making stuff, he likes to travel and play board games.
Follow him on Instagram or Twitter


Score ACE Events
EventScorer implements Heng Ji's 3 event metrics, as defined in
Refining Event Extraction [Ji and Grishman, ACL 2008]. The
three metrics are trigger labeling, argument identification,
and argument role labeling. These metrics are applied at the
event mention level (not the event level).
It is invoked by
xjet
AceJet.EventScorer
docList textDirectory textExtension
systemApfDirectory systemApfExtension keyApfDirectory keyApfExtension");
where
- docList
- file containing list of document file names, one per line
- textDirectory
- directory containing text files for documents
- textExtension
- file extension for text files
- systemApfDirectory
- directory containing APF files to be scored
- systemApfExtension
- file extension for APF files to be scored
- keyApfDirectory
- directory containing APF keys
- keyApfExtension
- file extension for APF keys
If one line in the docList file is d, the program will
compare the APF files
systemApfDirectory/d.systemApfExtension and
keyApfDirectory/d.keyApfExtension.


Assigning arguments in UI events
When zoom level changed, the contents of infoWindow also updates.
Change the zoom level


#assign findIterate= "true">
Execute the query iterating over the results. This is good for processing large results where we want to avoid holding all the beans/results in memory but instead iterator through them.
Note that findIterate
(and findEach and findEachWhile) uses a "per graph"
persistence context scope and adjusts jdbc
fetch buffer size for large
queries. As such it is better to use findList for small queries.
Remember that with QueryIterator you must call close()
when you have finished iterating the results (typically in a finally block).
findEach() and findEachWhile() are preferred to findIterate
as they ensure
the jdbc
statement and resultSet
are closed at the end of the iteration.
This query will execute against the EbeanServer
that was used to create it.
JDBC
resources.
We should use try-with-resources
or a try-finally
block to make sure that the QueryIterator is closed regardless of exceptions thrown during processing.
Note that findEach and findEachWhile ensure all resources are closed regardless and for this reason they are recommended over findIterate
.
There are some use cases where findEach and findEachWhile don't naturally fit and that is when we would look to use findIterate
.
try-finally
```java
Querytry-with-resources
```java
try (QueryIterator